Heuristic Approaches for Solving Container Loading Problem 田邦廷、吳泰熙
E-mail: [email protected]
ABSTRACT
Container loading problems are frequently encountered in industries such as manufacturing, food and logistics. A good utilization of containers can always result in cost savings. This problem hence attracts attention from practitioners and researchers. Container loading problems are of the NP-Complete type, and hence can hardly be solved within an acceptable amount of time, especially for problems with larger sizes. The primary purpose of this research is to proposed heuristic methods to solve the problems in an efficient manner. A ” bottom-back-left ” packing approach is firstly presented, and later embedded in a simulated annealing and genetic algorithm, respectively. Computational results obtained from the comparison with those from the literature show the efficiency and efficacy of the proposed algorithms. Keywords：container, loading, simulated annealing, genetic algorithms.
Keywords : container ; loading ; simulated annealing ; genetic algorithms Table of Contents
封面內頁 簽名頁 碩士論文電子檔案上網授權書 iii 博碩士論文授權書 iv 中文摘要 v 英文摘要 vi 誌謝 vii 目錄 viii 圖目錄 xi 表目錄 xii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 研究假設與限制 3 1.4 研究方法與架構 4 第二章 文獻探討 6 2.1 長方體物件與長方體容器 6 2.2 演算法 8 2.3 其他 9 第三章 長方體物件堆疊問題求解 11 3.1 問題定義 11 3.2 物件堆疊 方向分析 11 3.3 使用率 13 3.4 「下後左角」優先之啟發式堆疊之建立 14 3.5 物件堆疊之特性 19 3.6 模擬退火演算法(SA) 20 3.6.1 起始解 21 3.6.2 移步 22 3.6.3 退火程序(Annealing Procedure) 23 3.6.4 終止條件 25 3.7 遺傳基因演算法(GA) 26 3.7.1 編碼 26 3.7.2 染色體數與產生母體 27 3.7.3 複製 30 3.7.4 交配 31 3.7.5 突變 34 3.7.6 終止條件 35 3.7.7 遺傳基因演算流程 35 第四 章 演算結果與分析 36 4.1 SA實驗分析 36 4.1.1 SA實驗參數與例題說明 36 4.1.2 SA實驗結果 37 4.2 GA實驗分析 39 4.2.1 GA 實驗參數與例題說明 40 4.2.2 GA實驗結果 40 4.3 執行結果與分析 42 4.3.1單一尺寸容器執行結果與分析 42 4.3.2多種尺寸容 器執行結果與分析 44 4.3.3 物件堆疊順序執行結果與分析 45 4.3.4 物件選取範圍執行結果與分析 46 第五章 結論 48 5.1 結論 48 5.2 建議 49 參考文獻 50 附錄一 53 附錄二 55 附錄三 59 附錄四 61 附錄五 63
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